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MSDM: Generating Task-Specific Pathology Images with a Multimodal Conditioned Diffusion Model for Cell and Nuclei Segmentation

Dominik Winter, Mai Bui, Monica Azqueta Gavaldon, Nicolas Triltsch, Marco Rosati, Nicolas Brieu

TL;DR

MSDM tackles data scarcity in cell and nuclei segmentation by generating targeted synthetic image-mask pairs through a multimodal diffusion model conditioned on morphology maps HV, color statistics, and metadata encoded by BERT. It extends semantic diffusion models with SPADE conditioning and cross-modal attention to steer synthesis toward specific morphologies, exemplified on columnar cells. Quantitative analysis shows synthetic data improves real-data segmentation performance and closely matches real data distributions under matched conditions, outperforming vanilla SDMs. The approach demonstrates the value of multimodal diffusion for robust, scalable data augmentation in computational pathology.

Abstract

Scarcity of annotated data, particularly for rare or atypical morphologies, present significant challenges for cell and nuclei segmentation in computational pathology. While manual annotation is labor-intensive and costly, synthetic data offers a cost-effective alternative. We introduce a Multimodal Semantic Diffusion Model (MSDM) for generating realistic pixel-precise image-mask pairs for cell and nuclei segmentation. By conditioning the generative process with cellular/nuclear morphologies (using horizontal and vertical maps), RGB color characteristics, and BERT-encoded assay/indication metadata, MSDM generates datasests with desired morphological properties. These heterogeneous modalities are integrated via multi-head cross-attention, enabling fine-grained control over the generated images. Quantitative analysis demonstrates that synthetic images closely match real data, with low Wasserstein distances between embeddings of generated and real images under matching biological conditions. The incorporation of these synthetic samples, exemplified by columnar cells, significantly improves segmentation model accuracy on columnar cells. This strategy systematically enriches data sets, directly targeting model deficiencies. We highlight the effectiveness of multimodal diffusion-based augmentation for advancing the robustness and generalizability of cell and nuclei segmentation models. Thereby, we pave the way for broader application of generative models in computational pathology.

MSDM: Generating Task-Specific Pathology Images with a Multimodal Conditioned Diffusion Model for Cell and Nuclei Segmentation

TL;DR

MSDM tackles data scarcity in cell and nuclei segmentation by generating targeted synthetic image-mask pairs through a multimodal diffusion model conditioned on morphology maps HV, color statistics, and metadata encoded by BERT. It extends semantic diffusion models with SPADE conditioning and cross-modal attention to steer synthesis toward specific morphologies, exemplified on columnar cells. Quantitative analysis shows synthetic data improves real-data segmentation performance and closely matches real data distributions under matched conditions, outperforming vanilla SDMs. The approach demonstrates the value of multimodal diffusion for robust, scalable data augmentation in computational pathology.

Abstract

Scarcity of annotated data, particularly for rare or atypical morphologies, present significant challenges for cell and nuclei segmentation in computational pathology. While manual annotation is labor-intensive and costly, synthetic data offers a cost-effective alternative. We introduce a Multimodal Semantic Diffusion Model (MSDM) for generating realistic pixel-precise image-mask pairs for cell and nuclei segmentation. By conditioning the generative process with cellular/nuclear morphologies (using horizontal and vertical maps), RGB color characteristics, and BERT-encoded assay/indication metadata, MSDM generates datasests with desired morphological properties. These heterogeneous modalities are integrated via multi-head cross-attention, enabling fine-grained control over the generated images. Quantitative analysis demonstrates that synthetic images closely match real data, with low Wasserstein distances between embeddings of generated and real images under matching biological conditions. The incorporation of these synthetic samples, exemplified by columnar cells, significantly improves segmentation model accuracy on columnar cells. This strategy systematically enriches data sets, directly targeting model deficiencies. We highlight the effectiveness of multimodal diffusion-based augmentation for advancing the robustness and generalizability of cell and nuclei segmentation models. Thereby, we pave the way for broader application of generative models in computational pathology.

Paper Structure

This paper contains 14 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Methods
  4. MSDM: Extending the semantic diffusion model
  5. Multimodal feature fusion
  6. Experiments
  7. Model training
  8. Datasets
  9. Dataset enhancement with the diffusion model
  10. Evaluation
  11. Results
  12. Discussion
  13. Compliance with ethical standards
  14. Acknowledgments

Figures (3)

  • Figure 1: MSDM overview: We condition the model on cell and nuclei morphologies using horizontal and vertical (HV) maps, and on RGB color characteristics (distinguishing foreground/background pixels). Additionally, BERT-encoded textual information conditions the diffusion model via a multi-head cross-attention mechanism to generate synthetic images that match the morphology of existing segmentation masks, creating a new image-mask pair for model training.
  • Figure 2: a Original and corresponding synthetic images, for visualization images with the same (rotated) target mask are shown b Distribution of cell eccentricities in the training-set before and after data augmentation with MSDM and in the two test-sets.
  • Figure 3: Wasserstein distances in latent space embeddings between real (y-axis) and synthetic (x-axis) images corresponding to different combination of assay and indication pairs (e.g. 1:=HER2/Breast,...). Matching pairs lie on the diagnonal of the so-formed matrices.